1.1. Introduction to Maps and Spatial Data

These examples use the GeoPandas package which adds special functions for DataFrames to work with geographic data.

There are two basic ways to visualize the data spatial data (aka make maps): plot() and explore(). plot() creates a matplotlib chart of the data, explore() creates an interactive map with the shapes overlayed on open map data.

For the visualizations to work, though, you have to set a relevant index on the data. In our case here, the index is the district number.

After the basic plots, we merge the school district data with the school demographics data in order to create more detailed examples.

Useful links:

[10]:

import pandas as pd
import geopandas as gpd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from IPython.display import Markdown as md

from nycschools import schools, exams, ui, geo

[11]:

# read the GeoJSON file directly from the download link
gdf = geo.load_districts()
# rename the columns
# gdf.columns = ['district', 'area', 'length', 'geometry']
# each shape in "geometry" represents a distict
gdf = gdf.set_index("district")
gdf

[11]:
area length geometry
district
32 51898496.7618 37251.0574964 MULTIPOLYGON (((-73.91181 40.70343, -73.91290 ...
16 46763620.3794 35848.9043428 MULTIPOLYGON (((-73.93312 40.69579, -73.93237 ...
17 128440514.645 68356.1032412 MULTIPOLYGON (((-73.92044 40.66563, -73.92061 ...
13 104871082.804 86649.0984086 MULTIPOLYGON (((-73.97906 40.70595, -73.97924 ...
25 443759165.29 176211.272136 MULTIPOLYGON (((-73.82050 40.80101, -73.82040 ...
29 420204870.637 135100.006491 MULTIPOLYGON (((-73.73816 40.72896, -73.73716 ...
10 282540516.283 94957.3984139 MULTIPOLYGON (((-73.86790 40.90294, -73.86796 ...
26 424302397.116 131618.853059 MULTIPOLYGON (((-73.74461 40.77895, -73.74429 ...
18 175191726.482 120925.187236 MULTIPOLYGON (((-73.86327 40.58388, -73.86381 ...
15 196154191.734 153439.205153 MULTIPOLYGON (((-73.98633 40.69105, -73.98536 ...
27 790725033.478 572076.106693 MULTIPOLYGON (((-73.83193 40.59345, -73.83117 ...
8 258762275.331 222001.724191 MULTIPOLYGON (((-73.83979 40.83562, -73.83967 ...
7 92258856.3652 65300.6110934 MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ...
9 82980717.7665 44396.4418588 MULTIPOLYGON (((-73.93309 40.82823, -73.93307 ...
22 385697616.013 270256.625384 MULTIPOLYGON (((-73.91990 40.59960, -73.91996 ...
1 35160467.8524 28647.0153555 MULTIPOLYGON (((-73.97177 40.72582, -73.97179 ...
2 279512458.191 207556.482699 MULTIPOLYGON (((-74.04388 40.69019, -74.04351 ...
3 113412690.658 51922.3297145 MULTIPOLYGON (((-73.95672 40.78660, -73.95716 ...
31 1604156793.41 430016.498898 MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ...
14 150297861.39 96653.7952568 MULTIPOLYGON (((-73.95440 40.73911, -73.95428 ...
4 52620569.229 52062.8830951 MULTIPOLYGON (((-73.92134 40.80085, -73.92114 ...
11 393216114.225 304563.048828 MULTIPOLYGON (((-73.78833 40.83467, -73.78931 ...
12 69088427.7096 48691.7772088 MULTIPOLYGON (((-73.88284 40.84782, -73.88200 ...
21 210183056.106 123821.906082 MULTIPOLYGON (((-73.96185 40.62757, -73.96139 ...
6 96346045.8858 68850.7104133 MULTIPOLYGON (((-73.92641 40.87762, -73.92635 ...
24 394976460.434 127338.082622 MULTIPOLYGON (((-73.90642 40.73987, -73.90648 ...
28 247533101.13 115703.38462 MULTIPOLYGON (((-73.84485 40.73575, -73.84480 ...
19 199878572.903 173955.448638 MULTIPOLYGON (((-73.84674 40.60485, -73.84672 ...
23 47399296.4018 40315.8019548 MULTIPOLYGON (((-73.92137 40.66549, -73.92143 ...
5 52518652.0916 44503.2919823 MULTIPOLYGON (((-73.93516 40.83268, -73.93513 ...
30 318184623.011 151669.593359 MULTIPOLYGON (((-73.90647 40.79018, -73.90251 ...
20 242738512.599 94300.7864855 MULTIPOLYGON (((-74.02553 40.65148, -74.02491 ...
10 3282963.15136 7883.37233723 MULTIPOLYGON (((-73.90893 40.87216, -73.90898 ... 
[12]:

# draw the basic map using the tab20b color map
# the x and y axes are the lat/long coordinates of the shapes
_ = gdf.plot(figsize=(16, 16), cmap="tab20b")
../../_images/nb_05-Maps-and-GIS_01-maps-intro_3_0.png 
[13]:

# explore gives us an interactive map
# non-geo columns show up when you hover over a shape
gdf.explore()

[13]:
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[14]:

# load the demographics
# group the data at the district level
# merge the two dataframes on the district

dist_map = gdf.copy()

demo = schools.load_school_demographics()
demo = demo.query(f"ay == {demo.ay.max()}")
aggs = {
    'total_enrollment': 'sum',
    'asian_pct': 'mean',
    'black_pct': 'mean',
    'hispanic_pct': 'mean',
    'white_pct': 'mean',
    'swd_pct': 'mean',
    'ell_pct': 'mean',
    'poverty_pct': 'mean'
}
demo = demo.groupby("district").agg(aggs)


cols = [c for c in demo.columns if c.endswith("pct")]
for col in cols:
    demo[col]= demo[col].apply(lambda x: f"{round(x*100, 2)}")

dist_map = dist_map.reset_index()
dist_map.district = pd.to_numeric(dist_map.district, downcast='integer', errors='coerce')


dist_map = dist_map.join(demo, on="district", how="inner")
dist_map = dist_map.set_index("district")


dist_map = dist_map[['geometry', 'total_enrollment', 'asian_pct',
       'black_pct', 'hispanic_pct', 'white_pct', 'swd_pct', 'ell_pct',
       'poverty_pct']]
dist_map

[14]:
geometry total_enrollment asian_pct black_pct hispanic_pct white_pct swd_pct ell_pct poverty_pct
district
32 MULTIPOLYGON (((-73.91181 40.70343, -73.91290 ... 9831 1.97 15.87 78.73 2.61 21.46 24.09 87.66
16 MULTIPOLYGON (((-73.93312 40.69579, -73.93237 ... 7127 2.4 68.88 21.85 4.72 27.09 5.87 86.54
17 MULTIPOLYGON (((-73.92044 40.66563, -73.92061 ... 20628 3.23 72.76 17.7 3.67 21.67 10.56 83.58
13 MULTIPOLYGON (((-73.97906 40.70595, -73.97924 ... 24564 6.45 54.65 22.49 12.66 20.81 6.3 69.2
25 MULTIPOLYGON (((-73.82050 40.80101, -73.82040 ... 38992 49.74 6.43 30.36 11.21 15.4 20.81 68.98
29 MULTIPOLYGON (((-73.73816 40.72896, -73.73716 ... 23434 12.62 64.65 15.85 1.8 17.79 8.52 73.02
10 MULTIPOLYGON (((-73.86790 40.90294, -73.86796 ... 54253 4.86 15.74 73.14 4.53 21.93 23.35 86.15
10 MULTIPOLYGON (((-73.90893 40.87216, -73.90898 ... 54253 4.86 15.74 73.14 4.53 21.93 23.35 86.15
26 MULTIPOLYGON (((-73.74461 40.77895, -73.74429 ... 30663 54.71 9.72 16.47 15.48 15.09 9.94 50.69
18 MULTIPOLYGON (((-73.86327 40.58388, -73.86381 ... 14725 2.04 82.66 10.14 3.55 24.35 7.6 81.25
15 MULTIPOLYGON (((-73.98633 40.69105, -73.98536 ... 29806 13.39 15.87 37.15 28.82 23.07 13.28 57.15
27 MULTIPOLYGON (((-73.83193 40.59345, -73.83117 ... 50409 19.04 24.99 40.28 10.03 19.13 11.2 76.61
8 MULTIPOLYGON (((-73.83979 40.83562, -73.83967 ... 26180 6.12 22.74 64.51 4.93 23.38 14.31 85.25
7 MULTIPOLYGON (((-73.89681 40.79581, -73.89694 ... 16672 1.08 26.92 69.13 1.48 25.97 16.47 91.57
9 MULTIPOLYGON (((-73.93309 40.82823, -73.93307 ... 31605 1.66 27.12 68.65 1.48 23.87 22.55 91.88
22 MULTIPOLYGON (((-73.91990 40.59960, -73.91996 ... 36602 16.19 39.49 17.66 23.84 20.65 13.46 72.34
1 MULTIPOLYGON (((-73.97177 40.72582, -73.97179 ... 10553 13.9 18.36 49.84 14.25 26.78 8.41 72.2
2 MULTIPOLYGON (((-74.04388 40.69019, -74.04351 ... 73667 17.06 16.52 36.37 24.73 20.15 8.89 56.16
3 MULTIPOLYGON (((-73.95672 40.78660, -73.95716 ... 24943 7.11 24.73 37.02 25.68 22.51 5.99 56.87
31 MULTIPOLYGON (((-74.05051 40.56642, -74.05047 ... 59648 11.88 13.77 32.02 39.47 25.44 8.57 60.49
14 MULTIPOLYGON (((-73.95440 40.73911, -73.95428 ... 16739 4.49 21.69 58.51 12.63 23.49 11.29 75.62
4 MULTIPOLYGON (((-73.92134 40.80085, -73.92114 ... 11754 7.01 25.49 59.13 5.18 26.83 10.96 82.63
11 MULTIPOLYGON (((-73.78833 40.83467, -73.78931 ... 40363 7.21 37.13 45.17 8.27 21.31 12.09 81.76
12 MULTIPOLYGON (((-73.88284 40.84782, -73.88200 ... 18732 2.78 24.78 69.42 1.5 23.46 20.57 92.18
21 MULTIPOLYGON (((-73.96185 40.62757, -73.96139 ... 37486 23.48 14.11 28.81 31.24 19.2 21.33 75.89
6 MULTIPOLYGON (((-73.92641 40.87762, -73.92635 ... 19167 1.32 6.11 84.92 6.12 22.25 29.4 84.57
24 MULTIPOLYGON (((-73.90642 40.73987, -73.90648 ... 55320 16.59 2.69 66.36 12.82 17.69 25.09 77.67
28 MULTIPOLYGON (((-73.84485 40.73575, -73.84480 ... 40144 29.39 23.61 27.1 13.1 15.82 12.22 67.96
19 MULTIPOLYGON (((-73.84674 40.60485, -73.84672 ... 20951 7.39 45.28 43.2 2.03 22.59 13.51 89.96
23 MULTIPOLYGON (((-73.92137 40.66549, -73.92143 ... 8661 1.35 70.83 23.65 1.63 26.78 5.48 90.75
5 MULTIPOLYGON (((-73.93516 40.83268, -73.93513 ... 12223 3.91 44.55 39.61 8.7 27.47 7.54 79.07
30 MULTIPOLYGON (((-73.90647 40.79018, -73.90251 ... 40939 19.09 6.55 53.41 18.15 15.29 24.65 73.07
20 MULTIPOLYGON (((-74.02553 40.65148, -74.02491 ... 54920 47.33 1.82 26.72 22.9 16.78 27.05 79.07 
[15]:

# make a plot that uses the poverty percent as a the value for the color map
# this is the `column` keyword argument for plot()


fig, ax = plt.subplots(figsize=(16, 16))
sns.set_context('talk')

# don't show the boundary box or x/y ticks
plt.axis('off')
fig.tight_layout()

ax.set_title('School District Poverty Levels', pad=20)

# put the district number in the center of each district
def label(row):
    xy=row.geometry.centroid.coords[0]
    ax.annotate(row.name, xy=xy, ha='center', fontsize=14)
dist_map.apply(label, axis=1)

_ = dist_map.plot(legend="True", ax=ax, column="poverty_pct", cmap="coolwarm")
../../_images/nb_05-Maps-and-GIS_01-maps-intro_6_0.png 
[16]:

# if we pass a different column, we can create a different plot
fig, ax = plt.subplots(figsize=(16, 16))
sns.set_context('talk')
plt.axis('off')
fig.tight_layout()
ax.set_title('School District Enrollments Levels (size)', pad=20)
dist_map.plot(legend="True", ax=ax, column="total_enrollment", cmap="autumn_r")
plt.show()
../../_images/nb_05-Maps-and-GIS_01-maps-intro_7_0.png 
[17]:

# explore also has a column keyword
# here we show the districts by poverty level
# mouse over and you can see all of the demographic data for the district
dist_map.explore(column="poverty_pct", cmap="coolwarm")

[17]:
Make this Notebook Trusted to load map: File -> Trust Notebook